Crash detection

tools/sklearn/crash_detection.py

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+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+# ================================
+# 1. PREPARE DATA
+# ================================
+df = pd.read_feather("/home/jerome/Perso/freqtradeDocker/user_data/data/binance/BTC_USDC-1h.feather")
+
+# features
+df['returns'] = df['close'].pct_change()
+df['atr'] = (df['high'] - df['low']).rolling(14).mean()
+df['slope'] = df['close'].rolling(20).mean().diff()
+
+df['drawdown'] = (df['close'] - df['close'].rolling(48).max()) / df['close'].rolling(48).max()
+
+# label : crash si -12% dans les 48h
+future = df['close'].shift(-48)
+df['future_dd'] = (future - df['close']) / df['close']
+df['crash'] = (df['future_dd'] < -0.12).astype(int)
+
+df = df.dropna()
+
+X = df[['returns','atr','slope','drawdown']]
+y = df['crash']
+
+# ================================
+# 2. TRAIN MODEL
+# ================================
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
+
+model = RandomForestClassifier(n_estimators=200)
+model.fit(X_train, y_train)
+
+print("Accuracy:", model.score(X_test, y_test))
+print("Feature importance:", model.feature_importances_)